Source code for cornac.models.c2pf.recom_c2pf

# -*- coding: utf-8 -*-
"""
@author: Aghiles Salah <asalah@smu.edu.sg>
"""

import numpy as np
import scipy.sparse as sp
from ..recommender import Recommender
import c2pf


# Recommender class for Collaborative Context Poisson Factorization (C2PF)
[docs]class C2PF(Recommender): """Collaborative Context Poisson Factorization. Parameters ---------- k: int, optional, default: 100 The dimension of the latent factors. max_iter: int, optional, default: 100 Maximum number of iterations for variational C2PF. aux_info: array, required, shape (n_context_items,3) The item-context matrix, noted C in the original paper, \ in the triplet sparse format: (row_id, col_id, value). variant: string, optional, default: 'c2pf' C2pf's variant: c2pf: 'c2pf', 'tc2pf' (tied-c2pf) or 'rc2pf' (reduced-c2pf). \ Please refer to the original paper for details. name: string, optional, default: None The name of the recommender model. If None, \ then "variant" is used as the default name of the model. trainable: boolean, optional, default: True When False, the model is not trained and Cornac assumes that the model already \ pre-trained (Theta, Beta and Xi are not None). init_params: dictionary, optional, default: {'G_s':None, 'G_r':None, 'L_s':None, 'L_r':None, \ 'L2_s':None, 'L2_r':None, 'L3_s':None, 'L3_r':None} List of initial parameters, e.g., init_params = {'G_s':G_s, 'G_r':G_r, 'L_s':L_s, 'L_r':L_r, \ 'L2_s':L2_s, 'L2_r':L2_r, 'L3_s':L3_s, 'L3_r':L3_r}, \ where G_s and G_r are of type csc_matrix or np.array with the same shape as Theta, see below). \ They represent respectively the "shape" and "rate" parameters of Gamma distribution over \ Theta. It is the same for L_s, L_r and Beta, L2_s, L2_r and Xi, L3_s, L3_r and Kappa. Theta: csc_matrix, shape (n_users,k) The expected user latent factors. Beta: csc_matrix, shape (n_items,k) The expected item latent factors. Xi: csc_matrix, shape (n_items,k) The expected context item latent factors multiplied by context effects Kappa, \ please refer to the paper below for details. References ---------- * Salah, Aghiles, and Hady W. Lauw. A Bayesian Latent Variable Model of User Preferences with Item Context. \ In IJCAI, pp. 2667-2674. 2018. """ def __init__(self, k=100, max_iter=100, aux_info=None, variant='c2pf', name=None, trainable=True, init_params={'G_s': None, 'G_r': None, 'L_s': None, 'L_r': None, 'L2_s': None, 'L2_r': None, 'L3_s': None, 'L3_r': None}): if name is None: Recommender.__init__(self, name=variant.upper(), trainable=trainable) else: Recommender.__init__(self, name=name, trainable=trainable) self.k = k self.init_params = init_params self.max_iter = max_iter self.ll = np.full(max_iter, 0) self.eps = 0.000000001 self.Theta = None # user factors self.Beta = None # item factors self.Xi = None # context factors Xi multiplied by context effects Kappa self.aux_info = aux_info # item-context matrix in the triplet sparse format: (row_id, col_id, value) self.variant = variant # fit the recommender model to the traning data
[docs] def fit(self, X): """Fit the model to observations. Parameters ---------- X: scipy sparse matrix, required the user-item preference matrix (traning data), in a scipy sparse format\ (e.g., csc_matrix). """ # recover the striplet sparse format from csc sparse matrix X (needed to feed c++) (rid, cid, val) = sp.find(X) val = np.array(val, dtype='float32') rid = np.array(rid, dtype='int32') cid = np.array(cid, dtype='int32') tX = np.concatenate((np.concatenate(([rid], [cid]), axis=0).T, val.reshape((len(val), 1))), axis=1) del rid, cid, val if self.variant == 'c2pf': res = c2pf.c2pf(tX, X.shape[0], X.shape[1], self.aux_info, X.shape[1], X.shape[1], self.k, self.max_iter, self.init_params) elif self.variant == 'tc2pf': res = c2pf.t_c2pf(tX, X.shape[0], X.shape[1], self.aux_info, X.shape[1], X.shape[1], self.k, self.max_iter, self.init_params) elif self.variant == 'rc2pf': res = c2pf.r_c2pf(tX, X.shape[0], X.shape[1], self.aux_info, X.shape[1], X.shape[1], self.k, self.max_iter, self.init_params) else: res = c2pf.c2pf(tX, X.shape[0], X.shape[1], self.aux_info, X.shape[1], X.shape[1], self.k, self.max_iter, self.init_params) self.Theta = sp.csc_matrix(res['Z']).todense() self.Beta = sp.csc_matrix(res['W']).todense() self.Xi = sp.csc_matrix(res['Q']).todense()
[docs] def score(self, user_index, item_indexes = None): """Predict the scores/ratings of a user for a list of items. Parameters ---------- user_index: int, required The index of the user for whom to perform score predictions. item_indexes: 1d array, optional, default: None A list of item indexes for which to predict the rating score.\ When "None", score prediction is performed for all test items of the given user. Returns ------- Numpy 1d array Array containing the predicted values for the items of interest """ if self.variant == 'c2pf' or self.variant == 'tc2pf': if item_indexes is None: user_pred = self.Beta * self.Theta[user_index, :].T + self.Xi * self.Theta[user_index, :].T else: user_pred = self.Beta[item_indexes,:] * self.Theta[user_index, :].T + self.Xi * self.Theta[user_index, :].T elif self.variant == 'rc2pf': if item_indexes is None: user_pred = self.Xi * self.Theta[user_index, :].T else: user_pred = self.Xi[item_indexes,] * self.Theta[user_index, :].T else: if item_indexes is None: user_pred = self.Beta * self.Theta[user_index, :].T + self.Xi * self.Theta[user_index, :].T else: user_pred = self.Beta[item_indexes,:] * self.Theta[user_index, :].T + self.Xi * self.Theta[user_index, :].T # transform user_pred to a flatten array, user_pred = np.array(user_pred, dtype='float64').flatten() return user_pred
[docs] def rank(self, user_index, known_items = None): """Rank all test items for a given user. Parameters ---------- user_index: int, required The index of the user for whom to perform item raking. known_items: 1d array, optional, default: None A list of item indices already known by the user Returns ------- Numpy 1d array Array of item indices sorted (in decreasing order) relative to some user preference scores. """ u_pref_score = np.array(self.score(user_index)) if known_items is not None: u_pref_score[known_items] = None rank_item_list = (-u_pref_score).argsort() # ordering the items (in decreasing order) according to the preference score return rank_item_list